Early Labour Market Outcomes of Recent Canadian University Graduates by Discipline: A Longitudinal, Cross-cohort Analysis
Bibliographic record
Abstract
This paper reports the results of an empirical analysis of the early career outcomes of recent Canadian Bachelor's level graduates by discipline based on three waves of the National Graduates Surveys, which comprise large, representative databases of individuals who successfully completed their programmes at Canadian universities in 1982, 1986, and 1990, with information gathered during interviews conducted two and five years after graduation for each group of graduates (1984/87, 1988/92, 1990/95). The outcomes analysed, all broken down by sex and discipline, include: the distribution of graduates by field and the percentage of female graduates; the percentage of graduates who subsequently completed another educational programme; the overall evaluation of the choice of major (would they choose it again?); unemployment rates, the percentage of workers in part-time jobs, in temporary jobs, self-employed; the job-education skill and credentials matches; earnings levels and rates of growth; and job satisfaction (earnings, overall). Many of the outcomes conform to expectations, typically reflecting the different orientations of the various disciplines with respect to direct career preparedness, with the professions and other applied disciplines generally characterised by lower unemployment rates, closer skill and qualification matches, higher earnings, and so on. On the other hand, while the "applied" fields also tend to perform well in terms of the "softer", more subjective measures regarding job satisfaction and the overall evaluation of the chosen programme (would the graduate choose the same major again?), the findings also indicate that graduates' assessments of their post-graduation experiences and overall evaluations of the programmes from which they graduated are based on more than simply adding up standard measures of labour market "success", with the job satisfaction scores and - perhaps most interestingly - the overall programme evaluations often departing from what the objective measures (unemployment rates, earnings levels, etc.) might have predicted. Some implications of the findings are discussed and avenues for future research are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".